Method and system for producing statistical analysis of medical care information

ABSTRACT

A method and system for producing statistical analysis of medical care information comprises: aggregating medical care providers to a peer group level; aggregating medical care information at the peer group level and at the medical care provider level; computing a statistical analysis, such as performing Pearson&#39;s correlation analysis; and generating peer group level and medical care provider level results utilizing the computed statistical analysis. Also, a method for producing statistical analysis of medical care information for a medical care provider efficiency measurement comprises: applying minimum unit of analysis criteria for medical care providers to be used in statistical analysis; calculating an overall weighted average medical care information measure for each medical care provider; calculating a medical condition-specific medical care information measure for each medical care provider; removing outlier medical care providers from statistical analysis at medical care information level; calculating a statistical analysis to medical care provider efficiency measurement at each medical care information level using a statistical calculation; and selecting statistically related medical care information to identify medical care providers meeting a desired practice pattern.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/172,728 filed Feb. 4, 2014, which is a Continuation-in-Part ofco-pending U.S. patent application Ser. No. 13/621,222 filed Sep. 15,2012, which is a continuation of U.S. patent application Ser. No.12/473,147, filed May 27, 2009 and issued as U.S. Pat. No. 8,301,464 onOct. 30, 2012, which claims priority to our provisional patentapplication entitled “Method And System For Analyzing PhysicianEfficiency Scores To Identify Reasons For Inefficient And EfficientPractice Patterns”, with application No. 61/082,080, and filed Jul. 18,2008, all incorporated herein by reference. Moreover, application Ser.No. 14/172,728, of which this application is a continuation as statedabove, also claims priority to our provisional patent applicationentitled “Method And System For Analyzing Physician Efficiency Scores ToIdentify Reasons For Inefficient And Efficient Practice Patterns”, withapplication No. 61/867,577, filed Aug. 19, 2013, all incorporated hereinby reference. Moreover application Ser. No. 14/172,728, of which thisapplication is a continuation as stated above, also is a continuation ofco-pending U.S. patent application Ser. No. 13/970,564, filed Aug. 19,2013, which is a continuation-in-part of co-pending U.S. patentapplication Ser. No. 13/621,222 filed Sep. 15, 2012, all incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention generally relates to analyzing health careinformation and, more specifically, to a system and method of producingstatistical analysis of medical care information for a medical careprovider efficiency measurement. The method comprises calculating astatistical analysis to medical care provider efficiency measurement atan overall weighted average and at each medical care information levelusing a statistical calculation; and selecting statistically relatedmedical care information to identify medical care providers meeting adesired practice pattern.

BACKGROUND OF THE INVENTION

Health care costs continue to rise at a rapid rate and total nationalhealth expenditures are expected to rise at twice the rate of inflationin 2008. U.S. health care spending is expected to increase at similarlevels for the next decade.

One factor contributing to rising health care costs is due to 10% to 20%of physicians, across specialty types, practicing inefficiently.Efficiency means using an appropriate amount of medical resources in anappropriate setting to treat a medical condition or given number ofmedical conditions, and achieving a desired health outcome and qualityof patient care. Thus, efficiency is a function of unit price, volume ofservice, intensity of service, and quality of service. The inefficientpractitioners are often those 10% to 20% of practitioners by specialtytype utilizing significantly more services to treat a given grouping ofpatients with equivalent medical conditions or condition-specificepisodes of care as compared to their immediate peer group or bestpractice guideline. The inefficient practitioners can be responsible fordriving 10% to 20% of the unnecessary, excess, medical expendituresincurred by employers and other health care purchasers, equating tobillions of dollars nationally.

Currently health plans, insurance companies, third party administrators(TPAs), health maintenance organizations, and other health firms (whichcollectively shall be called “health plans”) expend a significant amountof technical, clinical, and analytical resources trying to identify theinefficient practitioners.

Once health plans have identified inefficient practitioner, they realizethat each practitioner has a different practice pattern to deal with andhas its own little ‘microcosm’ of practice. At the microcosm level, manyclinical and analytical resources are currently expended trying todetermine the microcosm practice patterns for each practitioner for eachspecialty type. The result is that health plans may end up managinghundreds of different practice patterns which is time and resourceintensive and makes monitoring over time difficult.

It is often extremely difficult and costly to identify and target theone or two services most associated with practitioner efficiency.Different practice patterns of each practitioner as well as theinability to easily identify services most associated with practitionerefficiency, make it challenging and costly for health plans to embark onstrategies to reduce expenditure and improve quality. Programs such astargeted practitioner education and behavioral change, Pay forPerformance (P4P) and value-based benefit plan design become moreresource intensive and costly and less effective due to difficulties inknowing where to focus and areas to target for improvements.Additionally, the lack of focus results in challenges in monitoring andmeasuring improvements over time.

BRIEF SUMMARY OF THE INVENTION

A method and system for producing statistical analysis of medical careinformation comprises: aggregating medical care providers to a peergroup level; aggregating medical care information at the peer grouplevel and at the medical care provider level; computing a statisticalanalysis, such as performing Pearson's correlation analysis; andgenerating peer group level and medical care provider level resultsutilizing the computed statistical analysis.

Also, a method for producing statistical analysis of medical careinformation for a medical care provider efficiency measurementcomprises: applying minimum unit of analysis criteria for medical careproviders to be used in statistical analysis; calculating an overallweighted average medical care information measure for each medical careprovider; calculating a medical condition-specific medical careinformation measure for each medical care provider; removing outliermedical care providers from statistical analysis at medical careinformation level; calculating a statistical analysis to medical careprovider efficiency measurement at each medical care information levelusing a statistical calculation; and selecting statistically relatedmedical care information to identify medical care providers meeting adesired practice pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph showing a Positive Correlation Example;

FIG. 2 is a graph showing a Negative Correlation Example;

FIG. 3 is an exemplary Sub-Service Detail Correlation Report, inaccordance with one embodiment of the present invention;

FIG. 4 shows an exemplary MedMarker Checkout Report, in accordance withthe embodiment shown in FIG. 3;

FIG. 5 shows a MedMarker Target Report, in accordance with theembodiment shown in FIG. 4;

FIG. 6 is an exemplary Practitioner Efficiency Report, in accordancewith one embodiment of the present invention;

FIG. 7 is an exemplary Service Prevalence Report, in accordance with theexample shown in FIG. 6;

FIG. 8 is an exemplary Procedure Code Report for one specialty, inaccordance with the invention and example shown in FIG. 7;

FIGS. 9 and 10 are flowcharts illustrating exemplary operation of oneembodiment of the present invention; and

FIG. 11 is a block diagram illustrating a General Purpose Computer, suchas utilized to implement the present invention, as shown in FIGS. 9 and10.

DETAILED DESCRIPTION OF THE INVENTION

A Grouper system uses medical care information to build medicalcondition-specific episodes. Once these condition-specific episodes ofcare are built, then the episodes are examined through an EfficiencyCaresystem.

Efficiency means using an appropriate amount of medical resources in anappropriate setting to treat a medical condition or a given number ofmedical conditions, and achieve a desired quality of patient care. Thus,efficiency is a function of unit price, volume of service, intensity ofservice, and may include a quality of service component. Volume refersto the number of services performed to treat a specific medicalcondition (e.g., an office visit, two laboratory tests, and oneprescription drug). Intensity refers to the magnitude of medical careordered to treat a medical condition (e.g., an x-ray versus a computedtomography scan).

The end result there is typically a score between 0.70 and 1.50. Thisscore reflects the resources a health care provider uses in treating agrouping of patients with medical conditions or condition-specificepisodes of care as compared to their immediate peer group or a bestpractice guideline. If a health care provider receives a score of 0.70,then that health care provider is using 30% fewer resources as comparedto the peer group.

The Grouper system generates three primary data sets: Assign.tab dataset that assigns episodes of care to health care providers;PatientCLI.tab data set that contains patient claim line items (CLI);and EpMaster.tab data set that contains episodes of care information.The EfficiencyCare system utilizes the Assign.tab data set to generate:a Score.tab data set that includes health care provider efficiencyscores; a Detail.tab data set that provides health care providerefficiency score details; and an ProvEp.tab data set that provideshealth care provider efficiency episodes. The present inventionprimarily involves a BullsEye system that utilizes those data setsdescribed above to generate a BullsEyeMB.tab and BullsEyeMCID.tab dataset that targets medical care information most related to or indicativeof health care provider efficiency and inefficiency.

There are three input files to one embodiment of the present invention.One of these input files comes from the Grouper system, and it's calledthe Patient CLI File 42. This file contains all the claim line itemsfrom the CLI Input File, but with the claims organized by medicalcondition episode of care. In one embodiment, 11 additional pieces ofinformation are added to each claim line item (CLI), and this is thePatient CLI File. These additional pieces of information are added forease of data mining.

The other two input files for one embodiment of the present inventionare output files from EfficiencyCare system. One of these files is theDetail.tab File 68. A record in this file is the health care provider(e.g. physician).

The other file is called the ProvEP.tab File 44, which is an episodefile, and it contains all the final episodes of care that made itthrough EfficiencyCare system and into the Detail.tab file 68. In thisembodiment, the ProvEP.tab File 44 is preferred to have because itcontains the episode identifiers in this file that allow the presentinvention to tie back the Claim Line Items (CLIs) in the Patient CLIFile.

In one embodiment of the present invention the ProvEP.tab File 44 isused to identify the episode IDs for a health care provider, and it isthese episode IDs that are assigned to the health care provider (e.g.physician) and used to calculate his or her efficiency score. Then, thepresent invention data mines over into the PatientCLI.tab File 42 tofind out the CPT-4 codes responsible for a provider's 1.25 or 1.40efficiency score. That is, determining why the provider is using more orfewer services. However, there are hundreds of potential CPT-4 codesthat could be the cause, because a large number of different medicalconditions are typically being examined for each health care provider.So, the present invention uses a statistical measure, such as aPearson's Correlation (a statistic that associates two variables—in thiscase it is typically the health care provider's efficiency score fromEfficiencyCare system (other statistical tools, models, anddistributions are also within the scope of this invention)), to aprocedure or service (e.g., CPT-4 or HCPCS code) score. The closer to1.00, the stronger the association (with a Pearson's correlationcoefficient). So, the present invention typically reviews large numbersof potential procedure or service (e.g., CPT-4 or HCPCS) codes thatcould potentially be a primary cause of efficiency or inefficiency, andthen determines that a clinical leader should really just focus on asmall number (e.g. 2 to 5) of procedure or service codes because theseare the procedure or service codes that tend to be most associated withthose health care provider's efficiency scores that are high, forexample, 1.20 and above, or low. But, also note that these sameprocedure or service codes identify procedures that efficient providersare doing much less of. Thus, these MedMarkers (i.e., procedures orservice codes associated with provider efficiency scores) may also beused to identify efficient health care providers as well. This is whytypically MedMarkers are those procedures or services that areassociated with provider efficiency scores. And note that health careprovider efficiency scoring is preferably done on a specialty byspecialty basis, so cardiologists are evaluated separately from generalinternists and separately from pediatricians.

The present invention “automates” the process for targeting theseMedMarkers. That is, analysts at a health plan, physician group, or anyother organization might be able to look for these associations by doinga specialized three month study, and then determining the procedures andservices (e.g., CPT-4 and HCPCS) associated with the efficiency score ofhealth care providers for a specialty type. This is a long process. Thepresent invention provides software, methods, and algorithms thatautomate this process, greatly reducing the time needed to find theseassociations, as well as increasing the accuracy of the results.

After selecting the MedMarkers, the present invention then targets thehealth care providers that meet the specialty-specific practice patternas reflected by the MedMarkers selected by a user. It can then presentthe specified MedMarker results (rates per episode of care) for thehealth care provider as compared to the selected peer group.

The present invention saves information technology (IT) resources,statistician and analyst resources, and clinical resources needed by ahealth plan, physician group, or any other organization to identifythese important MedMarkers. The process is automated.

Also, once these MedMarkers are known, then the health plan, physiciangroup, or any other organization can take action (i.e., implementstrategies that fit each health plan, physician group, or any otherorganization's philosophies for reducing practice patterns variation) toimprove efficiency through working with the health care providers toreduce variability in the identified MedMarkers, focus health carepayment reform with respect to the MedMarkers, and implement health planbenefit plan design changes such as adding in deductibles or copaymentsfor the MedMarkers to make the consumer more aware of those services(i.e., MedMarkers) associated with inefficient health care providerpractice patterns.

The following personnel in a health plan or physician group can usethese MedMarkers to improve medical management performance: medicaldirectors to work with network health care providers to improveperformance; health care analysts and informatics specialists thatexamine claims data to observe reasons for health care provider practicepattern differences or variation; health care actuaries that want tounderstand services and procedures (i.e., MedMarkers) to target tochange health care provider reimbursement to reduce adverse incentivesfor health care providers to perform more of a certain service orprocedure.

One embodiment of the present invention utilizes ASCII tab-delimiteddatabase output files from the Grouper system and the EfficiencyCaresystem. There are the Detail.tab 68, PatientCLI.tab 42, and ProvEP.tab44 Files. Then, this embodiment, using these input files, produces twointermediate output files, ProvCLI.tab and MinProvEp.tab. Theseintermediate output files are then used to produce two final outputfiles, BullsEyeMB.tab and BullsEyeMCID.tab. Other file and datastructures are also within the scope of the present invention, includingdatabases.

The present invention is the first to use statistical techniques thatautomates the process for identifying only those procedures and services(e.g., CPT-4 and HCPCS codes) that are most associated with the healthcare provider efficiency score. One of the unexpected advantages is thatthe MedMarkers are often unexpected, and sometimes evencounter-intuitive.

Also, in other embodiments of the present invention:

-   -   In the preferred embodiment, only services and procedures are        analyzed.

However, in another embodiment, drug prescriptions are analyzed in asimilar manner.

-   -   In another embodiment, there may be a spreadsheet that loads the        user identified MedMarkers, the MedMarker service rate per        episode, the targeted lower MedMarker rate per episode, the        average allowed charge amount for each MedMarker, and the        prevalence rate of the medical condition. The spreadsheet can        then calculate potential savings for the user using the below        formula:        Savings Calculation=Current MedMarker services per        episode(−)Target MedMarker services per episode(×)Average        allowed charge per service(×)Number of episodes    -   In another embodiment, Service Code Groups are built. In one        example, two unique CPT-4 codes for skin biopsy (11100        and 11101) may be examined separately, and therefore, perform a        Pearson's correlation on them separately. But, in another        embodiment, they are combined together into a specific Service        Code Group, which is this case can be called Skin        Biopsies=11100+11101. The rates per episode would also be        combined and the present invention would be run only after        Service Code Groups are formed to find MedMarkers. Here are some        possible Service Code Groups:        -   Destruction of Premalignant Lesions=17000+17004 (these are            two of several CPT-4 codes corresponding to Destruction of            Premalignant Lesions)        -   Shave Skin Lesions=11300+11301+11305+11310 (these are some            of the several CPT-4 codes corresponding to Shave Skin            Lesions)

Calculating the Pearson's Correlations, eventually, on Service CodeGroups in some situations may result in more meaningful results to auser than just inspecting each CPT-4 code result individually. Note thatthe CPT-4 codes in a Service Code Group often look very similar in termsof their verbal description—because they are. For example, under theDestruction of Premalignant Lesions, it may be that code 17000 is usedfor destroying fewer than 15 lesions, and code 17004 is used for billingpurposes for destroying more than 15 lesions. One can see on the verbaldescription for the codes that code 17004 has +15 lesions on it. Thus,these codes are very similar, and sometimes are just volume oriented.Here's another potential Service Code Group:

-   -   Upper Gastrointestinal (GI) Endoscopy=43239+43235 (these are two        of several CPT-4 codes corresponding to Upper GI Endoscopy),        whereby:        -   43239=Upper GI Endoscopy with biopsy        -   43239=Upper GI Endoscopy, diagnosis without biopsy            Thus, here, the determination is not made based on numbers,            but instead a moderate procedure type difference which is            having a biopsy present or not. However, this still would            potentially be a good Service Code Group.

One embodiment of the present invention is made up of four components:

-   -   The Grouper system groups unique ICD.9 diagnosis codes into 526        meaningful medical conditions based on clinical homogeneity with        respect to generating a similar clinical response from health        care providers treating a patient.    -   The EfficiencyCare system is health care provider efficiency        measurement software that takes the output from the Grouper        system and develops specialty-specific health care provider        efficiency scores that compare individual health care provider        efficiency against the efficiency of a peer group of interest or        practice pattern of interest.    -   Correlation Calculation Software takes output from the Grouper        system and EfficiencyCare system and performs correlation        analysis of health care providers' service, sub-service, and        procedure or service code scores as compared to their efficiency        score.    -   A Reporting Dashboard, Other Reports, and Open Architecture        Output Files. The Reporting Dashboard produces correlation        summary reports by service category, sub-service category, and        procedures and service code. Reports may include a MedMarker        Selection/Summary Report, and Health Care Provider Summary        Report. Embodiments of the present invention also provides other        reports at key points during processing. All reports are based        on output files accessible to the user, and these output files        may be used for additional client-developed analysis.

There are several ways that the present invention may be used to addvalue to an organization. The present invention rapidly targetsMedMarkers (i.e., those few procedures and services most associated withhealth care provider efficiency scores). Knowing these MedMarkers, thepresent invention identifies health care providers meeting anorganization's established MedMarker criteria. On drill-down, the usergenerally knows the established MedMarker practice patterns peridentified health care provider.

Next, users can identify a practice pattern (preferably per specialtytype) that identifies inefficient health care providers. Therefore, theymay develop and educate their medical management staff on a standard,MedMarker-based, practice pattern. This enables an organization'smedical management staff to cost-effectively implement and monitor onestandard health care provider feedback program.

Moreover, MedMarkers identified by the present invention identifypotential areas of significant procedure and service over-utilization,upcoding, and unbundling. Therefore, MedMarkers may serve as a sourcefor potential health care provider fee payment adjustments by specialtytype per region. Here are some examples:

-   -   Potential over-utilization example: Dermatologists receiving an        inefficient score perform more skin biopsies for the same        grouping of medical conditions.    -   Potential upcoding example: Dermatologists receiving an        inefficient score upcode their office visits from 10 minutes to        15-or-20 minutes.    -   Potential unbundling example: Dermatologists performing a skin        biopsy receive payment for both a 20 minute office visit and the        skin biopsy, increasing their payment over 300% as compared to a        10 minute office visit alone.

An organization now can have explicit procedures and services to improveits current health care provider payment system by implementing changesto reduce over-utilization, upcoding, and unbundling.

Furthermore, health services research shows that health care providerefficiency measurement methodologies often falsely identify some healthcare providers as inefficient, when in fact, the health care providersreally are efficient (“false positives”). As a result, health careproviders may be inappropriately excluded from high performance networksor not receive pay for performance bonuses.

For the first time, organizations can have an automated tool to validatethe accuracy of their health care provider efficiency scores. In orderfor each health care provider's score to be validated as accurate, theycan confirm that the health care provider has a higher MedMarkerutilization per episode (as compared to the peer group). The end resultwill typically be higher acceptance of results by network health careproviders, thereby reducing potential conflicts, as well as reducing theclinical and analyst resources used to justify the accuracy of eachscore.

The present invention uses the output from Grouper and EfficiencyCaresystems to develop specialty-specific correlations to health careprovider efficiency at the:

-   -   Service and sub-service category level    -   Medical condition level    -   Procedure or service code level,

There are several steps to identifying a MedMarker (i.e. a procedure andservice correlated to health care provider efficiency scores):

-   -   Apply minimum episode criteria for health care providers to be        used in correlation analysis.    -   For each health care provider, calculate an overall weighted        average service and sub-service category score.    -   For each health care provider, create a medical        condition-specific service and sub-service category score.    -   Calculate an overall weighted average procedure or service code        score for each health care provider.    -   Calculate a medical condition-specific procedure or service code        score for each health care provider.    -   If desired, remove outlier health care providers from analysis        at a service category, sub-service category, and procedure or        service code level.    -   Calculate the correlation to health care provider efficiency        scores at each level described above using a Pearson's        correlation calculation.    -   Correlated service and sub-service categories and procedures or        services can be selected as MedMarkers and used to identify        health care providers that meet a desired practice pattern.

These steps preferably occur after removing outlier episodes and healthcare providers that did not meet a minimum episode number establishedwhen running EfficiencyCare system. Therefore, outlier episodesidentified during efficiency analysis, and health care providers notreceiving an efficiency score, are not included in the analysis.

In one embodiment, a health care provider must have a minimum number ofnon-outlier episodes at the specialty-specific marketbasket level ormedical condition level in order to be included in the correlationanalysis. This minimum episode number should not be confused with aminimum episode number used to establish whether a health care providerreceives an efficiency score.

In one embodiment, each health care provider's overall weighted averageservice category utilization per episode is divided by the peer groupoverall weighted average service category utilization per episode tocalculate an overall service category score. Also, each health careprovider's overall weighted average sub-service category utilization perepisode is divided by the corresponding peer group's overall weightedaverage sub-service category utilization per episode to calculate anoverall sub-service category score.

NOTE: Overall utilization rates for service and sub-service categoriesmay be found in the EfficiencyCare system output file: Detail.tab.

In one embodiment, CPT-4 and HCPCS codes represent the procedure orservice code level detail that is used to report services per episoderate for the health care provider and the peer group. The presentinvention uses this information at the overall weighted average level tocalculate a unique procedure or service code score. Each health careprovider's procedure or service code per episode rate is divided by thecorresponding peer group procedure or service code per episode rate tocalculate an overall procedure or service code score. For example, adermatologist's overall skin biopsy rate per episode may be 0.477services per episode. The peer group skin biopsy per episode rate is0.175, resulting in a CPT-4 score for the dermatologist of a0.477/0.175=2.72.

Similar to the overall weighted average service and sub-service categoryscore, a medical condition-specific service category and sub-servicecategory utilization score are calculated for each health care provider.Each health care provider's condition-specific service categoryutilization per episode is divided by the peer group service categoryutilization per episode to calculate a condition-specific servicecategory score. Also, each health care provider's condition-specificsub-service category utilization per episode is divided by thecorresponding peer group sub-service category utilization per episode tocalculate a condition-specific sub-service category score.

NOTE: Medical condition-specific utilization rates for service andsub-service categories may be found in the EfficiencyCare system outputfile: Detail.tab.

In one embodiment, CPT-4 and HCPCS code detail may also be available formedical conditions within a market basket of interest. Thecondition-specific services per episode rate for the health careprovider and the peer group may be used to calculate a service codescore. For a specific medical condition, each health care provider'sservice code per episode rate is divided by the corresponding peer groupcondition-specific service code per episode rate to calculate a score.For example, a dermatologist's benign neoplasm of the skin biopsy rateper episode may be 0.500 services per episode. The peer group benignneoplasm of the skin biopsy rate per episode may be 0.250, resulting ina CPT-4 score for the dermatologist of a 0.500/0.250=2.00.

In the preferred embodiment health care provider outlier analysis ispreferably performed after health care providers receive a servicecategory score. The parameter SWITCH_BE_PROVOUTLIER in the run.iniconfiguration file defines the percent of health care providers thatwill be removed from correlation analysis in one embodiment of thepresent invention. Within each specialty marketbasket's servicecategory, a percentage of health care providers with the greatestabsolute variance between the health care provider's efficiency scoreand the service category score are removed from correlation analysis inthis embodiment. The health care provider outlier analysis removeshealth care providers having differences that are far away from a majorpart of the data. One reason for removing them is that those health careprovider outliers in the “difference area” may not be reliable from astatistical sense. Typically, the same health care providers are removedfrom sub-service category and procedure or service codes within thecorresponding service category for both the overall marketbasket leveland medical condition level correlation analysis. The health careproviders included in the correlation analysis may differ by servicecategory. For example, the health care provider outlier parameterdefault value may be 10%. Table 1 refers to a General Internist with anoverall efficiency score of a 0.90, and demonstrates if this health careprovider is to be included in correlation analysis for two separateservice categories. In other embodiments, other health care provideroutlier analysis methods may be utilized.

TABLE 1 General Internist Physician Outlier Example Include Physician inSer- Correlation Analysis? Overall vice (includes corresponding Effici-Cate- sub-service category and Service ency gory Absolute procedure orservice Category Score Score Variance level correlation analysis)Diagnostic 0.90 2.50 1.60 No. This physician is in the top Tests 10% ofphysicians with greatest variance. Medical/ 0.90 1.20 0.30 Yes. Thisphysician is not in the Surgical top 10% of physicians with the greatestvariance.

If the percent of health care providers removed as outliers cannot beachieved, then no health care providers are removed from the peer groupin one embodiment of the present invention. For example, if there are 6Allergists and 10% are to be removed, no health care providers areremoved from the Allergist marketbasket for correlation analysis.

Peer group substitution is preferably used for health care providers whohave passed the outlier criteria, but have not performed any services ina service category, sub-service category, or for a service code. Healthcare providers who did not receive a service category, sub-servicecategory, or procedure or service code score because they did notperform those services or procedures will receive a score of a 1.0,which represents the peer group results. For example, if a health careprovider did not perform any imaging tests, the health care provider'soverall weighted average sub-service category score for imaging wouldpreferably be substituted with a value of 1.0. In other embodiments,other peer group substitution methods may be utilized.

The main statistical analysis performed in one embodiment of the presentinvention is the Pearson's correlation analysis. Pearson's correlationanalysis is used to calculate the correlation of a service category,sub-service category, or procedure or service code to health careprovider efficiency score—Pearson's correlation coefficient (r). In thepresentation of the correlation results, the correlation coefficient (r)indicates the strength and direction of a linear relationship betweenthe dependant and independent variables, and varies from a low of −1.00to a high of 1.00. The higher the absolute value of the coefficient, thestronger the relationship between the two variables. In health servicesresearch, two variables may be considered fairly correlated if “r” isgreater than some limit (e.g., 0.20 or so). Also, two variables may beconsidered highly correlated if “r” is greater than some limit (e.g.,0.40 or so). Other statistical measurements are also within the scope ofthe present invention.

Correlation analysis is typically based on the identification of thedependent and independent variables which defines the detailed level foranalysis.

-   -   Dependent variable. The dependent variable in the correlation        model in the preferred embodiment of the present invention is a        health care provider's efficiency score. The dependent variable        is the health care provider's specialty-specific overall        weighted average efficiency score if looking at the overall        market basket level. The dependent variable is the health care        provider's specialty-specific and medical condition-specific        efficiency score if looking at the medical condition level.    -   Independent variables. There are three (3) types of independent        variables that are included in the preferred embodiment of the        present invention. These are listed in the following table.

TABLE 2 Potential Independent Variable Types Variable Types DefinitionService This is the service category score at either the overallCategory Score marketbasket level or the medical condition-specificlevel. In one embodiment, there are 11 service categories. Sub-ServiceThis is the sub-service category score at either the overall CategoryScore marketbasket level or the medical condition-specific level. In oneembodiment, there are 21 sub-service categories. Procedure or This is aprocedure or service code score at the overall Service Code marketbasketlevel or the medical condition-specific Score level. In one embodiment,the procedure or service code score is based on CPT-4 or HCPCS codes.

The Pearson's correlation coefficient (r) is used in one embodiment ofthe present invention to determine the strength of the relationshipbetween the health care provider efficiency score and health careprovider service category, sub-service category, and service code score.This coefficient provides a numeric measure of the strength of thelinear relationship between these two variables.

Pearson's correlation coefficient (r) ranges from a low of −1.00 to ahigh of 1.00. Positive correlations mean that the health care providerservice category, sub-service category, and service code scores arepositively associated with the health care provider efficiency score.That is, if a health care provider does more of the particular servicecode per episode as compared to the peer group, then the health careprovider most often has an efficiency score greater than a 1.00. Viceversa, if a health care provider does less of the particular servicecode per episode as compared to the peer group, then the health careprovider most often has an efficiency score less than a 1.00. Therefore,a positively correlated service code indicates that health careproviders performing more of this service code tend to have moreinefficient practice patterns as compared to the peer group. Negativecorrelations mean that the health care provider service category,sub-service category, and service code scores are negatively associatedwith the health care provider efficiency score. That is, if a healthcare provider does more of the particular service code per episode ascompared to the peer group, then the health care provider most often hasan efficiency score less than a 1.00. Vice versa, if a health careprovider does less of the particular service code per episode ascompared to the peer group, then the health care provider most often hasan efficiency score greater than a 1.00. Therefore, a negativelycorrelated service code indicates that health care providers performingmore of this service code tend to have more efficient practice patternsas compared to the peer group. Note that Pearson's correlationcoefficient is used in one embodiment of the present invention and isused here as an example of a measure of correlation. Other measures ofcorrelation are also within the scope of the present invention.

TABLE 3 Potential Correlation Intervals in Relation to EfficiencyCorrelation Range Correlation to Efficiency or Inefficiency   >0.40 Highpositive correlation to health care provider efficiency scores; the morehe does, the more likely the health care provider is to receive aninefficient score. 0.20 to 0.40 Good positive correlation to health careprovider efficiency scores −0.20 to 0.20   Low to no correlation tohealth care provider efficiency scores −0.20 to −0.40 Good negativecorrelation to health care provider efficiency scores <−0.40 Highnegative correlation to health care provider efficiency scores; the morehe does, the more likely the health care provider is to receive anefficient score.

FIG. 1 is a graph showing a Positive Correlation Example. In this FIG.,each procedure score for skin biopsies (CPT-4 11100) has been plottedagainst each dermatologist's overall health care provider efficiencyscore. When the CPT-4 score is high, the health care provider efficiencyscore is high. Alternatively, when the CPT-4 score is low, the overallefficiency score is low, resulting in a high Pearson's correlationcoefficient of a 0.64. According to Table 3 (above)—PotentialCorrelation Intervals in Relation to Efficiency, in this population,skin biopsies have a high positive correlation to health care providerefficiency scores, indicating a health care provider doing more of thisprocedure is more likely to receive an inefficient score.

FIG. 2 is a graph showing a Negative Correlation Example. In this FIG.,each procedure score for ECG Monitoring (CPT-4 93325) has been plottedagainst each Cardiologists' overall health care provider efficiencyscore. In this example, when the procedure score for ECG Monitoring(CPT-4 93325) for Cardiologists is high, the health care providerefficiency score is low. This is the opposite of the skin biopsy patternshown above. When the CPT-4 score is low, the overall efficiency scoreis high, resulting in a negative Pearson's correlation coefficient of a−0.26. According to Table 3 (above)—Potential Correlation Intervals inRelation to Efficiency, in this population, ECG Monitoring has a goodnegative correlation to health care provider efficiency scores,indicating a health care provider doing more of this procedure is morelikely to receive an efficient score.

A MedMarker is preferably a CPT-4 or HCPCS code that is relativelycorrelated to the health care provider efficiency score. To qualify as aMedMarker, the procedure or service should preferably have the followingproperties:

-   -   Good correlation (using Pearson's correlation “r” in this        example) to a health care provider specialty type's overall or        medical condition-specific efficiency score;    -   A higher prevalence rate per overall weighted average episode of        care, or medical condition-specific episode of care.    -   Clinical relevance in terms of medical support literature as to        when service should be performed; and    -   A reasonable charge per service (e.g., $50-to-$400 per service        in this example). The health care provider's condition-specific        efficiency score is a reflection of the services used to treat a        specific medical condition as compared to an immediate peer        group.

More than a given percentage of the health care providers (within thespecialty type of interest) perform one or more of the service code ofinterest.

The present invention allows an organization to identify one mainpractice pattern per specialty type per region that is most associatedwith health care provider efficiency scores, and identify those healthcare providers who meet this practice pattern.

-   -   Identify a MedMarker (or several MedMarkers) that will be used        to establish a practice pattern for specialty-specific health        care providers in a given region (see FIG. 4). Users can select        positively or negatively correlated MedMarkers (see FIG. 1 &        FIG. 2).    -   For the MedMarkers selected, define the percentage above or        below the services per episode rate to identify health care        providers with a specified practice pattern. For example, for        MRI of the lumbar region (CPT-4 72148), a general internist's        service per episode rate should be 10% higher than the peer        group rate for the health care provider to be defined as meeting        the practice pattern (see FIG. 5).    -   When selecting multiple MedMarkers to establish a practice        pattern, a threshold can be set for the amount of MedMarkers        that must meet or exceed the services per episode rate. For        example, if 7 MedMarkers are used to establish a practice        pattern, a user may only require 5 out of 7 MedMarker services        per episode rate be met in order to identify a health care        provider as matching a specified practice pattern (see FIG. 5).

The present invention will preferably produce a list of Provider IDsthat match the identified practice pattern (see FIG. 5). The Provider IDlist produced by the present invention can be loaded into EfficiencyCarePractitioner Efficiency Reports to further drill down on their practicepatterns.

FIGS. 6 through 8 are diagrams illustrating the process of identifyingMedMarkers, in accordance with one embodiment of the present invention.These examples are exemplary, and are only included here forillustrative purposes. It should be understood that these functions areautomated in a computer system in a preferred embodiment of the presentinvention, and the separate reports are shown merely to illustrate theprocess.

FIG. 6 is an exemplary Practitioner Efficiency Report, in accordancewith one embodiment of the present invention. It contains episodeinformation for one practitioner (i.e. health care provider) for anumber of medical conditions. For each medical condition, as well as aweighted average of all such medical conditions, averages for a peergroup of health care providers are also shown. For each medicalcondition and weighted average, there are a number of columns. It showsthe average charges per episode of care. The first column shows the nameof the medical condition. The next shows a Severity of Illness (SOI)level for the condition. This is followed by an episode count andaverage charger per episode. Then, the average charge per episode isbroken down across service categories in columns for: professionalvisits; diagnostic tests; lab/pathology; medical/surgical; prescriptions(Rx); facility outpatient; facility hospital; alternate sites; and othermedical expenses. An efficiency score is computed for the practitionerby dividing his average charge per episode of his average weightedcharges by the average charge per episode for the peer group. In thiscase, the average charge per episode for this practitioner was $567, andfor the peer group, it was $399. The quotient of these two averagecharges is 1.42, which can be utilized as an efficiency measurement.Other methods of and techniques for computing an efficiency measurementor score for health care providers are also within the scope of thepresent invention. In comparing this health care provider with others,this efficiency rating is in the 4th quartile, or 10th decile. Aquestion is asked, what CPT-4 code is most associated with theefficiency score? There are several steps outlined below to answer thisquestion. The first step is to identify a service category where thehealth care provider has significantly higher overall weighted averagecharges than the peer group. In this example, medical/surgical overallweighted average charge for the health care provider is significantlyhigher than the overall weighted average charge for the peer group asindicated by the asterisk on the practitioner weighted average resultfor Med/Surg, and is circled to illustrate this.

A next step is to drill-down to the service code level under sub-serviceambulatory surgical procedures to identify health care provider servicecodes with higher per episode rates than the peer group. FIG. 7 is anexemplary Service Prevalence Report in accordance with the example shownin FIG. 6. For the Dermatology specialty the report contains informationon services ordered for one healthcare provider and the peer group. Thereport also shows the number of unique episodes for the healthcareprovider and the peer group as well as the number of unique healthcareproviders in the peer group. Also, for both the healthcare provider andthe peer group, the number of services, number of services per episode,and the charge per service is shown for each service listed. There isalso a column showing services per episode percent difference from thepeer group.

Next, there is also a CPT-4 table shown in FIG. 7 (in the upper righthand quadrant) for CPT-4 11100. In the CPT-4 11100 table, the overallefficiency scores of several healthcare providers are shown for thisCPT-4 code (biopsy, skin lesion). In one embodiment, it would containentries for each healthcare provider in the peer group having treated asufficient number of episodes in the Dermatology marketbasket of medicalconditions. Also, a CPT-4 score is calculated for this CPT-4 code bydividing a healthcare provider number of services per episode by anaverage value for his peer group. The CPT-4 score for each healthcareprovider in the table is included in a CPT-4 score column along side hisefficiency score. In the case of the first Dermatologist in the table,overall efficiency score is 1.42, and the first Dermatologist has aCPT-4 score of 2.73. In one embodiment, this type of CPT-4 table isgenerated for each CPT-4 code being evaluated as a potential MedMarker.After the CPT-4 table is populated for a CPT-4 code, a statisticalmeasurement, such as a correlation coefficient (e.g. Pearson's “r”), iscomputed for the pairs of efficiency scores and CPT-4 scores for eachrow in the table. In one embodiment, a Pearson's coefficient is thestatistical measurement calculated. In other embodiments, other measuresof correlation or other statistical measurements may be utilized.

Finally, to identify the CPT-4 code most associated with efficiencyscores for the Dermatologists, FIG. 8 provides an exemplary ProcedureCode Report for the Dermatology specialty type, in accordance with theinvention and example shown in FIG. 7. This report shows one line foreach CPT-4 code being evaluated as a potential MedMarker for the givensub-service category of ambulatory surgical services. One example is thePearson's correlation computed for CPT-4 11100 shown in FIG. 7. Thefirst column in the report contains the statistical measurement (e.g.Pearson's correlation coefficient) calculated for pairs of efficiencyscores and CPT-4 scores for that CPT-4 code. The second column containsthe corresponding CPT-4 procedure code. This is followed by columns fora short name for the CPT-4 code, an average rate per episode for thiscode, and an average cost per procedure. The CPT-4 codes withsufficiently high positive or negative correlations are considered asMedMarkers. In this FIG. 8, CPT-4 procedure 11100 has a correlation of0.289, 11101 has a correlation of 0.218, 11401 has a correlation of0.302, and 11402 has a correlation coefficient of 0.221. These all havea correlation coefficient greater than 0.2, which is a exemplary cutoffin one implementation of the present invention, and these services,therefore, may be considered as potential MedMarkers. They all have arelatively high correlation between efficiency scores and CPT-4 scores.The remainder of the CPT-4 codes listed for this sub-service categoryhave lower correlation coefficients, are thus less correlated, and arepreferably eliminated from consideration as potential MedMarkers.

The MedMarker information presented in FIG. 8 is for sub-servicecategory of ambulatory surgical services across all medical conditionsin the Dermatology marketbasket. In one embodiment, MedMarkers can beidentified across all sub-service category services for a given medicalcondition (see FIG. 3) FIG. 3 is an exemplary Sub-Service DetailCorrelation Report, in accordance with one embodiment of the presentinvention. This report shows the correlation between different servicesand health care provider efficiency for a specialty (in this example,General Internist) and a specific medical condition (in this example,Low back pain). The fields in this report are:

Field Name Notes Top of Report Marketbasket This is the name of thespecialty-specific marketbasket selected for analysis. SOI - Aspecialty-specific marketbasket consists of the common Medical medicalconditions treated by each specialty type. This Condition field presentsthe medical condition name and the severity-of-illness (SOI) beingexamined. There are up to three SOI levels for each medical condition,with SOI-1 being the least severe (routine, non-complicated), and SOI-3being the most severe SOI (severity of illness). Aggregate MarketbasketSystem output contains information Group organized by aggregate groupsthat users define. This is the name of the aggregate group relevant tothe current data run. Correlation This is the cutoff value used todetermine what Cutoff procedures or services to display on theSub-Service Detail Report. This parameter is not applicable for theservice category and sub-service category level reports. Body of ReportColumns

This column allows you to select the MedMarkers of interest to add to auser's BullsEye “shopping cart”. Any service category or sub-servicecategory row can be selected by checking the box under this column. CorrThis column presents the correlation results of the service categoriesor sub-service categories to the health care provider's efficiencyscores at the overall marketbasket level or the medical condition levelwithin a marketbasket. Service/ This column presents the name of the 11service Sub-Service categories or 21 sub-service categories. CategoryNumber This column presents the total number of services for theServices specialty-specific peer group at the overall marketbasket levelor the medical condition level within a marketbasket. Service Units Thiscolumn presents the type of service associated with each servicecategory or sub-service category. For example, “Professional Visits”service type is office visits. Services per This column presents theaverage number of services per Episode episode for thespecialty-specific peer group at the overall marketbasket level or themedical condition level. Charge per This column presents the averagecharge per service for Service the specialty-specific peer group at theoverall marketbasket level or the medical condition level within amarketbasket. Unique This column presents the number of health careproviders Practitioners in the specialty-specific peer group at theoverall marketbasket level or the medical condition level within amarketbasket. Performing This column presents the percentage of thehealth care Practitioners providers in the peer group having performedthe service at least once at the overall marketbasket or the medicalcondition level.

As defined earlier in discussion of FIG. 5, FIG. 4 shows an exemplaryMedMarker Checkout Report, in accordance with the embodiment shown inFIG. 3. This report is a subset of the report shown in FIG. 3, withcolumns from that report selected by clicking under the marketbasketicon (

) in the first column.

FIG. 5 shows a MedMarker Target Report, in accordance with theembodiment shown in FIG. 4 as discussed earlier. A user first selects anumber of services as show in FIG. 4 by clicking under the marketbasketicon (

) in FIG. 3. The user then selects how many of the marketbasket servicesare required for a health care provider in this report (the report shownrequires one of the three) and a threshold based on the peer group. Thereport generated lists the practitioners who qualify under thesecriteria. The fields in this report are:

Field Name Notes Top of Report Aggregate Marketbasket System outputcontains information Group organized by aggregate groups that youdefine. This is the name of the aggregate group relevant to the currentdata run. Marketbasket This is the name of the specialty-specificmarketbasket selected for analysis. Medical A specialty-specificmarketbasket consists of the common Condition medical conditions treatedby each health care provider specialty type. This field presents themedical condition name being examined. If analysis is performed at themarketbasket level, this field will contain the value “all”. SOI Thisfield presents the severity-of-illness (SOI) being examined. There areup to three SOI levels for each medical condition, with SOL 1 being theleast severe (routine, non- complicated), and SOI-3 being the mostsevere SOI (severity of illness). If analysis is performed at theMarketbasket level, this field will be blank. Body of Report ColumnsPractitioner This is the unique identification number assigned to the IDhealth care provider analyzed. Practitioner This is the name of thehealth care provider analyzed. Name Efficiency This is the efficiencyscore for each health care provider. Score At the marketbasket level,the efficiency score is calculated by dividing the health careprovider's weighted average overall charges by the specialty-specificpeer group's weighted average overall charges. At the medicalcondition-SOI level, the efficiency score is calculated by dividing thehealth care provider's average medical condition-SOI charges by thespecialty-specific peer group's average medical condition-SOI chargeswithin a marketbasket. MedMarkers This column presents the number ofMedMarker criteria Meeting met by each health care provider. Criteria

A Practitioner MedMarker Report (not shown) provides users withadditional detailed information for each health care provider displayedin the MedMarker Target Report shown in FIG. 5. The MedMarker Targetreport has links for each practitioner, and when that link is selected,the details for each of the selected MedMarkers is shown for thatpractitioners.

FIGS. 9 and 10 are flowcharts illustrating exemplary operation of oneembodiment of the present invention. They are separated into twoflowcharts for illustrative purposes, and it should be understood thatthey may not be separate in different embodiments. Furthermore, filesare shown in these flowcharts. It should be understood this isillustrative and that other methods and techniques of data organizationand management are also within the scope of the present invention. Forexample, many of the operations shown may be implemented throughdatabase operations in place of file operations.

FIG. 9 starts by reading in a PatientCLI file 42, a ProvEp file 44, andRun.ini parameters 40. From these files, claims data fields areextracted for scored health care providers, step 46. From this, aProvider CLI assignment structure file is built, step 48, and a ReducedProvider Episode Structure Output file is built, step 50. Then, thefirst phase of files are written, step 50, including a ProvCLI file 54,a BullsEye file 56, and a MinProvEp file 58.

FIG. 10 starts by reading in clinical tables, step 70 from a MedCondfile 62, Specmb file 64, and MBConditions file 66. Also, data files areread in, step 68, including: a Detail file 68; the ProvCLI file 54; andthe MinProvEp file 58. The Run.ini run time parameters 40 are read in,and a sort is performed, step 72. A loop is entered, starting withreading a single ProvCLI record, step 74. Health care providers areaggregated to the peer group level, step 76. Claim Line Items areaggregated for service and subservice categories at the peer grouplevel, step 78 and at the provider level, step 80. An inner loop repeatsfor each CLI record, step 74. Then, data is prepared for statisticalanalysis, step 80, and statistical analysis, such as Pearson'scorrelation, is performed, step 84. Service and subservice categoryprovider and peer group records are written, step 86, and provider andpeer group records are written, step 88. An outer loop then repeats,starting at the beginning of the CLI records, step 74. At the end of theouter loop, the output files are written, step 90, including: aBullsEyeMB.tab file 92; a BullEyeMCID.tab file 94; a BullsEyeMB.txt file96; and a BullsEyeMCID.txt file 98.

Clinical MedMarker Protocol Ranges

Overview

Clinical MedMarker Protocol Ranges are achievable and appropriate rangesof clinical practice for the services and procedures that drive highercost of care by specialty type (i.e., MedMarker's). The MedMarkerservices also are process-of-care quality measures that are well-definedby clinical guidelines for many common medical conditions.

The Clinical MedMarker Protocol Ranges foster collaborative discussionsbetween health plans and other payers and the clinical leaders ofphysician groups and health systems. Such discussions concern whatconstitutes an achievable and appropriate practice range for aMedMarker. The ranges are based on the objective, collective experienceof CCGroup Specialist Panels and a National MedMarker ComparativeDatabase.

Recognizing Value Using Clinical MedMarker Protocol Ranges

Clinical MedMarker Protocol Ranges enable providers and provider groupsto improve the quality of care by identifying and reducing unwarrantedvariations in physician practice patterns, thus slowing the pace of costincreases. The ranges support value-based contracting efforts for bothpayers and health systems.

-   -   From the health plan and payer perspective, they can be used to:        -   Negotiate performance-based contracts for fee increases,            shared savings, and other value-based reimbursement or            risk-sharing programs.        -   Communicate to employers the cost-benefit of using providers            and groups within the clinically accepted protocol ranges.        -   Reduce costs and resources associated with preauthorization            programs for provider groups who stay within the Clinical            MedMarker Protocol Range.    -   From the health system or provider group perspective they help:        -   Achieve more favorable reimbursement and increase patient            volumes by staying within the established, achievable            Clinical MedMarker Protocol Ranges.        -   Improve the quality of care and reduce unwarranted practice            variations through more effective collaboration with payers.            Clinical MedMarker Protocol Range Methodology            Overview

Clinical MedMarker Protocol Ranges leverage two interlocking datasources to create acceptable clinical protocol ranges for many of themost common medical specialties, procedures, and diagnostic tests. Thetwo components are:

-   -   National MedMarker Comparative Database: A national comparison        of condition-specific MedMarker utilization rates.    -   National Specialist Panels: Nationwide panels of physicians by        specialty type; each specialty-specific panel is asked to review        the national condition-specific MedMarker utilization to provide        their expert opinion on appropriate levels of practice.

Definitions and Concepts

Clinical MedMarker Protocol Range: An achievable range for physicianpractice based on specialty-specific clinical input. The achievablerange applies to ordering or performing specific procedures ordiagnostic tests for prevalent and commonly treated medical conditions.

MedMarker: A CPT-4 or HCPCS code or set of codes that is/are highlycorrelated to the physician-efficiency score in treating a specificmedical condition. To qualify as a MedMarker, the procedure or serviceshould preferably have the following properties:

-   -   Good correlation (e.g. using Pearson's “r” correlation) to a        physician specialty type's overall or medical condition-specific        efficiency score.    -   A higher prevalence rate per overall weighted average episode of        care or medical condition-specific episode of care.    -   Clinical relevance in terms of medical support literature as to        when the service should be performed.    -   A reasonable charge per service, in a prespecified (e.g.        $50-to-$400) range.    -   More than a specified (e.g. 30%) of the physicians within the        specialty order or perform one or more of the services of        interest.

MedMarker correlations: A MedMarker can have positive or negativecorrelation to physician-efficiency scores. A positively correlatedMedMarker of greater than 0.20 is typically a procedure or service thathas good-to-high correlation to physician efficiency scores, indicatinga physician doing more of the procedure is more likely to receive anefficient score, while a negatively correlated. MedMarker is typically aprocedure or service that has good-to-high correlation to physicianefficiency scores, indicating a physician doing more of the procedure ismore likely to receive an inefficient score.

Episodes of care: All the diagnostic and therapeutic services (e.g.,ambulatory, outpatient, inpatient, facility, and prescription drugs)used to treat an individual's specific medical condition across acontiguous length of time (see episode duration) during which anindividual seeks care for that specific medical condition.

Episode duration: The length of time, in number of days, an episode ofcare lasted. The episode duration is a function of both the individual'scare-seeking behavior and the physician's treatment plan for thatindividual. The mean, 25th percentile, and 75th percentile episodedurations may be provided for each medical condition.

Episodes with MedMarker (percent): The percentage of all episodesattributed to a provider or provider group that had one or moreMedMarker services present.

Severity of Illness: Conditions may be evaluated for Severity of IllnessLevel-1 (SOI-1) or S01-2, using these definitions:

-   -   SOU: routine, uncomplicated; represents the least physiologic        progression (least severe).    -   S01-2: the disease may have local complications.    -   S01-3: the disease may involve multiple sites, or have systemic        complications (most severe).        National MedMarker Comparative Database

To identify medical condition-specific MedMarkers and ensure accuracyand sufficient sample size, an exemplary National MedMarker ComparativeDatabase may be applied to present the Percentage of Episodes withMedMarker service frequency to exemplary Specialist Panels' physicians.

The exemplary National MedMarker Comparative Database compiles claimsdata from a prespecified number (e.g. 25) of regions in the U.S. In thisexemplary database:

-   -   The data represents two years of the most recent health        insurance claims data.    -   Each region consists of at least a prespecified number (e.g.        200,000) members.    -   May tracks a prespecified (e.g. 64) MedMarkers spanning a        prespecified number (e.g. 20) of specialty types.    -   Physicians preferably require a minimum number of episodes to be        included in Percent of Episodes with MedMarker Service results        (e.g. 10 episodes).    -   Some individual physicians included in the database may see        large numbers of patients, and therefore represent practices        similar to a physician group. For this reason one may see the        term physician groups used in place of physicians.

Percentage of Episodes with MedMarker Service. This measure is:

-   -   One key to developing a protocol range is that this rate may        help eliminate differences in billing patterns, thereby creating        apples-to-apples comparison of frequency of MedMarkers.    -   Defined as the number of episodes with at least one MedMarker        performed (i.e. percentage of episodes with services).    -   Example: A physician may perform arthroscopies on 35% of her        episodes (or patients).

Another commonly calculated metric included in the exemplary NationalMedMarker Comparative Database is the Number of Services perprespecified (e.g. 1,000) Episodes. The Services per 1,000 Episodesmetric can be important to understanding physician practice patterns,including billing patterns. However, this metric may not answer thequestion of how often a service should be performed, and therefore wasnot included in the exemplary National Specialist Panel Surveys.

The Role of the Exemplary National Specialist Panels

Exemplary nationwide panels of physicians may be organized by specialtytype. Exemplary National Specialist Panels may consist of clinicianswho:

-   -   Are board-certified in the specialty or sub-specialty of        interest.    -   Have practiced 5-30 years after residency.    -   Spend at least 75% of their time in direct clinical practice and        patient care.    -   Have a current academic affiliation with a U.S. medical school        found in the top recipients of National Institutes of Health        clinical research funding for that medical specialty. Panels of        30 to 40 clinicians may be selected in each of the specialties        represented in exemplary Clinical MedMarker Protocol Ranges. In        some specialties, a good number of sub-specialists who tend to        perform or order the procedure of interest are also selected.

Each panel member may be asked to review the following information:

-   -   Definitions of the medical conditions and severity of illness        level of each patient population studied.    -   A definition of the MedMarker service of interest, including CPT        codes.    -   A frequency chart of Percentage of Episodes with MedMarker        Service derived from the exemplary National MedMarker        Comparative Database.        The survey instrument then may ask questions to obtain        appropriate clinical feedback.        Clinical MedMarker Protocol Range Methodology

The exemplary National Specialist Panels' results may be used to developan exemplary Clinical MedMarker Protocol Ranges, which are an achievablerange for physician practice based on specialty-specific clinical input.The results from the members of a National Specialist Panel are inputinto a computer system and statistics are calculated utilizing acomputer system based on those inputs. Two examples are shown for eachof two specialties.

Cardiology

Overview. 40 cardiologists were identified to participate in theexemplary Clinical MedMarker Protocol Range survey. All were affiliatedwith U.S. medical schools that rank among the top 40 recipients ofNational Institutes of Health clinical research funding for that medicalspecialty:

Example #1.1: Irritable Colon (SOI-1)—Colonoscopy

-   -   Brief Description of Condition: Irritable Colon SOU includes        irritable bowel syndrome and functional diarrhea. Irritable        Colon SOI-1 does not include functional digestive diseases,        diverticula of intestines, and noninfectious gastroenteritis or        colitis.    -   Average Duration: 25 days.    -   MedMarker of Interest: Colonoscopy.    -   Associated CPT Codes: 45355-45392.    -   Specialist Panel: N=31 gastroenterologists surveyed

Percent of Episodes with MedMarker Service Statistics: Mean Median Mode25th 50th 75th 11% 15% 0% 0% 15% 20%

Clinical MedMarker Protocol Range Clinical MedMarker Protocol RangeIrritable Colon (SOI-1) 0%-15% MedMarker: Colonoscopy

Question 1: Upper Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents 14%  7%  5% 30% 15% 10% 15% 15%(10% outliers removed) West 16%  7%  5% 30% 15% N/A N/A N/A Central 15%N/A 15% 15% 15% N/A N/A N/A Midwest  8%  8%  0% 15% 10% N/A N/A N/A East15% 12%  5% 45% 15% N/A N/A N/A South 20% 17%  5% 45% 15% N/A N/A N/A

Question 2: Lower Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents  4%  5%  0% 15%  5%  0%  5%  5%(10% outliers removed) West  8% 8%  0% 20%  5% N/A N/A N/A Central 10%N/A 10% 10% 10% N/A N/A N/A Midwest  2%  3%  0%  5%  0% N/A N/A N/A East 4%  7%  0% 25%  0% N/A N/A N/A South 13% 18%  0% 40%  5% N/A N/A N/A

Example #1.2: Noninfectious Gastroenteritis (SOI-1)—CT Abdomen/Pelvis

-   -   Brief Description of Condition: Noninfectious Gastroenteritis        SOI-1 includes non-infectious gastroenteritis, pseudopolyposis        of the colon, and eosinophilic gastroenteritis. Noninfectious        Gastroenteritis SOI-1 does not include eosinophilic or        ulcerative colitis, peritonitis, regional enteritis, Crohn's        disease, irritable bowel syndrome, or functional diarrhea.    -   Average Duration: 6 days    -   MedMarker of Interest: CT abdomen/pelvis    -   Associated CPT Codes: 72191-72194; 74150-74178    -   Specialist Panel: N=31 gastroenterologists surveyed

Percent of Episodes with MedMarker Service Statistics: Mean Median Mode25th 50th 75th 5% 5% 0% 0% 5% 10%

Clinical MedMarker Protocol Range Clinical MedMarker Protocol RangeNoninfectious Gastroenteritis (SOI-1) 0%-10% MedMarker: CTabdomen/pelvis

Question 1: Upper Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents  9%  3%  5% 15% 10% 5% 10% 10%(10% outliers removed) West  9%  6%  0% 20% 10% N/A N/A N/A Central 15%N/A 15% 15% 15% N/A N/A N/A Midwest  7%  6%  0% 10% 10% N/A N/A N/A East11%  9%  3% 40% 10% N/A N/A N/A South 11%  3% 10% 15% 10% N/A N/A N/A

Question 2: Lower Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents  2%  3%  0% 10%  0% 0% 0% 2% (10%outliers removed) West  6%  7%  0% 20%  5% N/A N/A N/A Central 10% N/A10% 10% 10% N/A N/A N/A Midwest  0%  0%  0%  0%  0% N/A N/A N/A East  1% 3%  0% 10%  0% N/A N/A N/A South  3%  2%  0%  5%  4% N/A N/A N/AOrthopedics

Overview

40 orthopedists were identified to participate in the Clinical MedMarkerProtocol Range survey. All were affiliated with U.S. medical schoolsthat rank among the top 40 recipients of National Institutes of Healthclinical research funding for that medical specialty:

Example #2.1: Bursitis of Upper Limb (SOI-1)—MRI Joint Extremities

-   -   Brief Description of Condition: Bursitis of Upper Limb SOI-1        includes bursitis of the olecranon, epicondylitis, ganglion, and        tenosynovitis of the hand. Bursitis of Upper Limb SOH does not        include enthesopathies, rotator cuff syndrome, or rupture of        tendon or synovium.    -   Average Duration: 38 days    -   MedMarker of Interest: MRI Joint Extremities    -   Associated CPT Codes: 73221-73223, 73721-73723    -   Specialists Panel: =40 orthopedists surveyed

Percent of Episodes with MedMarker Service Statistics: Mean Median Mode25th 50th 75th 18% 15% 15% 10% 15% 25%

Clinical MedMarker Protocol Range Clinical MedMarker Protocol RangeBursitis of Upper Limb (SOI-1) 0%-20% MedMarker: MRI joint extremities

Question 1: Upper Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents 22%  9% 10% 40% 20% 15% 20% 30%(10% outliers removed) West 24% 18%  0% 55% 25% N/A N/A N/A Central 27%23%  5% 70% 25% N/A N/A N/A Midwest 27%  6% 20% 30% 30% N/A N/A N/A East26% 14%  0% 50% 30% N/A N/A N/A South 18% 10%  5% 40% 15% N/A N/A N/A

Question 2: Lower Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents  7%  5%  0% 20%  5% 5% 5% 10% (10%outliers removed) West 15% 17%  0% 45%  5% N/A N/A N/A Central  4%  4% 0% 10%  5% N/A N/A N/A Midwest 10%  0% 10% 10% 10% N/A N/A N/A East 12% 9%  0% 30% 10% N/A N/A N/A South  4%  2%  0%  5%  5% N/A N/A N/A

Example #2.2: Low Back Pain (SOI-1)—MRI Spine

-   -   Brief Description of Condition: Low Back Pain 50I-1 includes        ankylosing spondylitis, spondylopathy, and sprain/strain along        the thoracic to coccyx. Low Back Pain 50I-1 does not include        schmorl's nodes, spondylosis, postlaminectomy syndrome, disc        displacement, or malignancies. There may be minor physiological        progression of spinal stenosis or disc degeneration, but these        diagnoses have not been documented.    -   Average Duration: 21 days    -   MedMarker of Interest: MRI spine    -   Associated CPT Codes: 72141, 72142, 72146-72149, 72156-72158    -   Specialists Panel: N=40 orthopedists surveyed

Percent of Episodes with MedMarker Service Statistics: Mean Median Mode25th 50th 75th 25% 20% 0% 10% 20% 40%

Clinical MedMarker Protocol Range Clinical MedMarker Protocol Range LowBack Pain (SOI-1) 0%-20% MedMarker: MRI spine

Question 1: Upper Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents 19%  6% 10% 35% 20% 15% 20% 20%(10% outliers removed) West 16% 10%  0% 35% 15% N/A N/A N/A Central 23%10% 10% 40% 20% N/A N/A N/A Midwest 20%  0% 20% 20% 20% N/A N/A N/A East26% 18%  5% 75% 20% N/A N/A N/A South 19% 12%  5% 50% 20% N/A N/A N/A

Question 2: Lower Bound Standard Statistics Percentile Mean S.D. Min MaxMedian 25th 50th 75th All Respondents  7%  5% 0% 20%  5% 5% 5% 10% (10%outliers removed) West  8% 10% 0% 30%  5% N/A N/A N/A Central  7%  6% 0%15% 10% N/A N/A N/A Midwest  7%  6% 0% 10% 10% N/A N/A N/A East 14% 15%0% 55% 10% N/A N/A N/A South  7%  8% 0% 25%  5% N/A N/A N/A

FIG. 11 is a block diagram illustrating a General Purpose Computer, suchas utilized to implement the present invention, as shown in FIGS. 9 and10. The General Purpose Computer 20 has a Computer Processor 22 (CPU),and Memory 24, connected by a Bus 26. Memory 24 is a relatively highspeed machine readable medium and includes Volatile Memories such asDRAM, and SRAM, and Non-Volatile Memories such as, ROM, FLASH, EPROM,EEPROM, and bubble memory. Also connected to the Bus are SecondaryStorage 30, External Storage 32, output devices such as a monitor 34,input devices such as a keyboard 36 with a mouse 37, and printers 38.Secondary Storage 30 includes machine-readable media such as hard diskdrives, magnetic drum, and bubble memory. External Storage 32 includesmachine-readable media such as floppy disks, removable hard drives,magnetic tape, CD-ROM, and even other computers, possibly connected viaa communications line 28. The distinction drawn here between SecondaryStorage 30 and External Storage 32 is primarily for convenience indescribing the invention. As such, it should be appreciated that thereis substantial functional overlap between these elements. Computersoftware such operating systems, utilities, user programs, and softwareto implement the present invention and data files can be stored in aComputer Software Storage Medium, such as memory 24, Secondary Storage30, and External Storage 32. Executable versions of computer software33, such as software utilized to implement the present invention can beread from a Non-Volatile Storage Medium such as External Storage 32.Secondary Storage 30, and Non-Volatile Memory and loaded for executiondirectly into Volatile Memory, executed directly out of Non-VolatileMemory, or stored on the Secondary Storage 30 prior to loading intoVolatile Memory for execution.

Those skilled in the art will recognize that modifications andvariations can be made without departing from the spirit of theinvention. Therefore, it is intended that this invention encompass allsuch variations and modifications as fall within the scope of theappended claims.

What is claimed is:
 1. A method implemented on a computer system ofidentifying medical care providers outside a process-of-care standardfor a field of health care, the computer system including at least oneprocessor, the method comprising: retrieving, by the at least oneprocessor, medical care information for a first plurality of medicalcare providers; calculating, by the at least one processor, a codemetric for one or more of the first plurality of medical care providersfor each of a plurality of code groups, each code metric for the codegroups derived from the medical care information, wherein: the medicalcare information includes claim line item information for at least 1,000episodes of care attributable to the first plurality of medical careproviders, the claim line item information including, in aggregate, atleast 100 codes each used to report a corresponding one of a medical,surgical, or diagnostic procedure or service, each of the code groups inthe plurality of code groups comprises one or more related codes fromamong the at least 100 codes, and the code metric for each of theplurality of code groups is based on a rate of utilization of therespective code group by the medical care provider compared to the rateof utilization of the respective code group across the first pluralityof medical care providers, calculating, by the at least one processorfor each of the code groups, an association value based on a pluralityof pairs of values, each pair of values consisting of: an efficiencymetric associated with a particular medical care provider, and the codemetric associated with the respective code group for the particularmedical care provider, selecting, by the at least one processor as amarker code group, one of the plurality of code groups for which theassociation value exceeds a threshold; retrieving, by the at least oneprocessor from a database, a protocol range for the rate of utilizationof the marker code group, the protocol range comprising upper and lowerbounds for the rate of utilization of the marker code group; generating,by the at least one processor, at least one output identifying one ormore of the first plurality of medical care providers having the rate ofutilization that falls outside the protocol range.
 2. The method inclaim 1 which further comprises: reading, by the at least one processor,medical claims information from a patient medical care information datasource; reading, by the at least one processor, medical care providerunit of analysis items from a medical care provider unit of analysisdata source; extracting, by the at least one processor, the medical careinformation from the medical claims information and the medical careprovider unit of analysis items for the first plurality of medical careproviders; building, by the at least one processor, a medical careprovider medical care information level item assignment structure fromthe medical care information; and building, by the at least oneprocessor, a reduced medical care provider unit of analysis structurefrom the medical care information for use in aggregating the medicalcare information.
 3. The method in claim 1 wherein retrieving themedical care information comprises: retrieving the medical careinformation aggregated at a plurality of levels.
 4. The method in claim3 further comprising: calculating the code metric at each of theplurality of levels; and generating, by the at least one processor foreach of the code groups, the association value at each of the pluralityof levels.
 5. The method in claim 1 which further comprises: computing astatistical analysis from medical care information on the computersystem; generating peer group level results utilizing the computedstatistical analysis on the computer system; and generating medical careprovider level results utilizing the computed statistical analysis onthe computer system; and calculating the efficiency metric through acomparison of medical care provider level results to peer group levelresults on the computer system.
 6. The method in claim 1 wherein each ofthe code groups comprise one of: a single procedure or service code; andtwo or more codes comprising variations of a same procedure or service.7. The method in claim 1 which further comprises: generating the atleast one output by performing at least one from a set consisting of:writing the identified one or more of the first plurality of medicalcare providers having the rate of utilization that falls outside theprotocol range to a nontransitory medium; and displaying the identifiedone or more of the first plurality of medical care providers having therate of utilization that falls outside the protocol range on a humanreadable medium.
 8. The method in claim 1, further comprisinggenerating, by the at least one processor, at least one reportidentifying a cost savings associated with a potential reduction in therate of utilization of the marker code group by at least one medicalcare provider to within the protocol range.
 9. The method in claim 8which further comprises: retrieving, by the at least one processor, anaverage allowed charge for the marker code group; and calculating thecost savings by subtracting a target rate of utilization of the markercode group within the protocol range from the rate of utilization by themedical care provider of the marker code group, and multiplying theresult by a number of the episodes attributable to the medical careprovider and by the average allowed charge.
 10. The method in claim 1,wherein generating the least one output includes linking, by the atleast one processor, each medical care provider of the one or more ofthe first plurality of medical care providers to a detail reportillustrating a variation in the rate of utilization of the respectivemedical care provider from the protocol range.
 11. The method in claim1, wherein the rate of utilization of the respective set of codes by themedical care provider and the rate of utilization of the respective setof codes across the first plurality of medical care providers isprovided with respect to the episodes of care for a specified medicalcondition.
 12. The method in claim 1, wherein the rate of utilization ofthe respective set of codes by the medical care provider and the rate ofutilization of the respective set of codes across the first plurality ofmedical care providers is provided with respect to the episodes of carefor a set of medical conditions associated with a practice category ofthe first plurality of medical care providers.
 13. The method in claim 1wherein the rate of utilization by the medical care provider and therate of utilization across the first plurality of medical care providersis based on a percentage of the episodes of care for care of a specifiedmedical condition in which any of the one or more related codes of thecode group was utilized.
 14. The method in claim 1 wherein the rate ofutilization by the medical care provider and the rate of utilizationacross the first plurality of medical care providers is based on apercentage of the episodes of care associated with a set of medicalconditions in which any of the one or more related codes of the codegroup was utilized, the set of medical conditions associated with apractice category of the first plurality of medical care providers. 15.The method in claim 1 wherein the rate of utilization by the medicalcare provider and the rate of utilization across the first plurality ofmedical care providers is based on a total number of instances ofutilization of any of the one or more related codes of the code groupper a specified number of episodes of care of a specified medicalcondition.
 16. The method in claim 1 wherein the rate of utilization bythe medical care provider and the rate of utilization across the firstplurality of medical care providers is based on a total number ofinstances of utilization of any of the one or more related codes of thecode group per a specified number of episodes of care of a set ofmedical conditions associated with a practice category of the firstplurality of medical care providers.
 17. The method in claim 1 whereinthe rate of utilization by the medical care provider and the rate ofutilization across the first plurality of medical care providers isbased on a total cost of utilization of any of the one or more relatedcodes of the code group per a specified number of the episodes of careof a specified medical condition.
 18. The method in claim 1 wherein therate of utilization by the medical care provider and the rate ofutilization across the first plurality of medical care providers isbased on a total cost of utilization of any of the one or more relatedcodes of the code group per a specified number of the episodes of careof a set of medical conditions associated with a practice category ofthe first plurality of medical care providers.
 19. The method in claim 1wherein the rate of utilization by the medical care provider and therate of utilization across the first plurality of medical care providersis based on a total number of instances of utilization of any of the oneor more related codes of the code group per number of patients treatedby the respective medical care provider.
 20. The method in claim 1wherein the rate of utilization by the medical care provider and therate of utilization across the first plurality of medical care providersis based on a total cost of utilization of any of the one or morerelated codes of the code group per number of patients treated by therespective medical care provider.
 21. The method in claim 1 wherein therate of utilization by the medical care provider and the rate ofutilization across the first plurality of medical care providers isbased on a total number of instances of utilization of any of the one ormore related codes of the code group per number of members treated bythe respective medical care provider, wherein a member is an individualto whom health care coverage has been extended under a health plan, andwherein treatment of a member comprises treatment of any of one or moreindividuals included in the member's health care coverage under thehealth plan.
 22. The method in claim 1 wherein the rate of utilizationby the medical care provider and the rate of utilization across thefirst plurality of medical care providers is based on a total cost ofutilization of any of the one or more related codes of the code groupper number of members treated by the respective medical care provider,wherein a member is an individual to whom health care coverage has beenextended under a health plan, and wherein treatment of a membercomprises treatment of any of one or more individuals included in themember's health care coverage under the health plan.
 23. The method inclaim 1 wherein at least one medical care provider of the firstplurality of medical care providers is an individual practitioner. 24.The method in claim 1 wherein at least one medical care provider of thefirst plurality of medical care providers is an aggregation ofindividual practitioners.
 25. The method in claim 24 wherein theaggregation of individual practitioners is associated with a healthplan.
 26. The method in claim 24 wherein the aggregation of individualpractitioners is associated with a geographic region.
 27. The method inclaim 1 wherein the upper and lower bounds for the rate of utilizationof the marker code group are each provided as a percentage of episodesof care in which any of the one or more related codes of the code groupis utilized.
 28. The method in claim 1 wherein the upper and lowerbounds for the rate of utilization of the marker code group are eachprovided as a total number of instances of utilization of any of the oneor more related codes of the code group per a specified number ofepisodes of care.
 29. The method in claim 1 wherein the upper and lowerbounds for the rate of utilization of the marker code group are eachprovided as a total cost of utilization of any of the one or morerelated codes of the code group per a specified number of episodes ofcare.
 30. The method in claim 1 wherein the upper and lower bounds forthe rate of utilization of the marker code group are each provided as atotal number of instances of utilization of any of the one or morerelated codes of the code group per number of patients treated by therespective medical care provider.
 31. The method in claim 1 wherein theupper and lower bounds for the rate of utilization of the marker codegroup are each provided as a total cost of utilization of any of the oneor more related codes of the code group per number of patients treatedby the respective medical care provider.
 32. The method in claim 1wherein the upper and lower bounds for the rate of utilization of themarker code group are each provided as a total number of instances ofutilization of any of the one or more related codes of the code groupper number of members treated by the respective medical care provider,wherein a member is an individual to whom health care coverage has beenextended under a health plan, and wherein treatment of a membercomprises treatment of any of one or more individuals included in themember's health care coverage under the health plan.
 33. The method inclaim 1 wherein the upper and lower bounds for the rate of utilizationof the marker code group are each provided as a total cost ofutilization of any of the one or more related codes of the code groupper number of members treated by the respective medical care provider,wherein a member is an individual to whom health care coverage has beenextended under a health plan, and wherein treatment of a membercomprises treatment of any of one or more individuals included in themember's health care coverage under the health plan.
 34. The method inclaim 1 wherein the association value is a Pearson's correlation. 35.The method in claim 34 wherein the threshold is 0.2.
 36. The method inclaim 34 wherein the threshold is 0.4.
 37. The method in claim 1 whereinthe association value is a Spearman's rank-order correlation.
 38. Themethod in claim 1 wherein the association value is a percent difference.39. The method in claim 1 wherein the step of selecting one of theplurality of codes as the marker code group further comprises:determining that the one of the code groups for which the associationvalue exceeds the threshold has been performed by at least a minimumpercentage of medical care providers in a practice category associatedwith the first plurality of medical care providers, prior to selectingthe one of the code groups as the marker code group.
 40. The method inclaim 39 wherein the minimum percentage is 30 percent.
 41. The method inclaim 1 wherein the one or more related codes are selected from amongprocedure codes and service codes in a designated medical care field.42. The method in claim 1 wherein the plurality of code groups numbersmore than
 100. 43. The method in claim 1 wherein the plurality of codegroups numbers more than
 300. 44. The method in claim 1 wherein the rateof utilization of the respective code group across the first pluralityof medical care providers is derived from at least 10,000 episodes of aset of medical conditions associated with a practice category of thefirst plurality of medical care providers.
 45. The method in claim 44wherein the first plurality of medical care providers numbers at least90.
 46. A method implemented on a computer system of identifying medicalcare providers outside a process-of-care standard for a field of healthcare, the computer system including at least one processor, the methodcomprising: calculating, by the at least one processor, an overallweighted average medical care information measure for each of a firstplurality of medical care providers; calculating, by the at least oneprocessor, a medical condition-specific medical care information measurefor each of the first plurality of medical care providers; removing, bythe at least one processor, outlier medical care providers fromstatistical analysis at medical care information level; calculating, bythe at least one processor, a statistical analysis to identify anindicator of an association between medical care information and medicalcare provider efficiency measurements comprising: calculating, by the atleast one processor, a code metric for one or more of the firstplurality of medical care providers for each of a plurality of codegroups, each code metric for the code groups derived from medical careinformation retrieved by the at least one processor, wherein: themedical care information includes claim line item information for atleast 1,000 episodes of care attributable to the first plurality ofmedical care providers, the claim line item information including, inaggregate, at least 100 codes each used to report a corresponding one ofa medical, surgical, or diagnostic procedure or service, each of thecode groups in the plurality of code groups comprises one or morerelated codes from among the at least 100 codes, and the code metric foreach of the plurality of code groups is based on a rate of utilizationof the respective code group by the medical care provider compared tothe rate of utilization of the respective code group across the firstplurality of medical care providers, calculating, by the at least oneprocessor, an efficiency metric for each of the first plurality ofmedical care providers using the overall weighted average medical careinformation measure for the respective medical care provider,calculating, by the at least one processor for each of the code groups,an association value based on a plurality of pairs of values, each pairof values consisting of: the efficiency metric associated with aparticular medical care provider, and the code metric associated withthe respective code group for the particular medical care provider,selecting, by the at least one processor as a marker code group, one ofthe plurality of code groups for which the association value exceeds athreshold; retrieving, by the at least one processor from a database, aprotocol range for the rate of utilization of the marker code group, theprotocol range comprising upper and lower bounds for the rate ofutilization of the marker code group; and generating, by the at leastone processor, at least one output identifying one or more of the firstplurality of medical care providers having the rate of utilization thatfalls outside the protocol range.
 47. The method in claim 46 whichfurther comprises: removing medical care providers failing to meet aminimum unit of analysis criteria before calculating the overallweighted average medical care information for each medical careprovider.
 48. The method in claim 47 wherein: the minimum unit ofanalysis is determined by a configuration parameter.
 49. The method inclaim 46 which further comprises: removing medical care providersfailing to meet a minimum unit of analysis criteria before calculatingmedical condition-specific medical care information for each medicalcare provider.
 50. The method in claim 46 wherein the association valueis a Pearson's correlation value.
 51. A computer system for identifyingmedical care providers outside a process-of-care standard for a field ofhealth care, the computer system comprising: a processor capable ofexecuting computer instructions; a memory coupled to the processorcontaining computer instructions for: retrieving medical careinformation for a first plurality of medical care providers; calculatinga code metric for one or more of the first plurality of medical careproviders for each of a plurality of code groups, each code metric forthe code groups derived from the medical care information, wherein: themedical care information includes claim line item information for atleast 1,000 episodes of care attributable to the first plurality ofmedical care providers, the claim line item information including, inaggregate, at least 100 codes each used to report a corresponding one ofa medical, surgical, or diagnostic procedure or service, each of thecode groups in the plurality of code groups comprises one or morerelated codes from among the at least 100 codes, and the code metric foreach of the plurality of code groups is based on a rate of utilizationof the respective code group by the medical care provider compared tothe rate of utilization of the respective code group across the firstplurality of medical care providers, calculating, for each of the codegroups, an association value based on a plurality of pairs of values,each pair of values consisting of: an efficiency metric associated witha particular medical care provider, and the code metric associated withthe respective code group for the particular medical care provider,selecting as a marker code group one of the plurality of code groups forwhich the association value exceeds a threshold; retrieving, from adatabase, a protocol range for the rate of utilization of the markercode group, the protocol range comprising upper and lower bounds for therate of utilization of the marker code group; and generating at leastone output identifying one or more of the first plurality of medicalcare providers having the rate of utilization that falls outside theprotocol range.
 52. A non-transitory recordable medium containingcomputer instructions for identifying medical care providers outside aprocess-of-care standard for a field of health care, wherein whenexecuted by a processor, said computer instructions cause the processorto perform steps of: retrieving medical care information for a firstplurality of medical care providers; calculating a code metric for oneor more of the first plurality of medical care providers for each of aplurality of code groups, each code metric for the code groups derivedfrom the medical care information, wherein: the medical care informationincludes claim line item information for at least 1,000 episodes of careattributable to the first plurality of medical care providers, the claimline item information including, in aggregate, at least 100 codes eachused to report a corresponding one of a medical, surgical, or diagnosticprocedure or service, each of the code groups in the plurality of codegroups comprises one or more related codes from among the at least 100codes, and the code metric for each of the plurality of code groups isbased on a rate of utilization of the respective code group by themedical care provider compared to the rate of utilization of therespective code group across the first plurality of medical careproviders, calculating, for each of the code groups, an associationvalue based on a plurality of pairs of values, each pair of valuesconsisting of: the efficiency metric associated with a particularmedical care provider, and the code metric associated with therespective code group for the particular medical care provider,selecting as a marker code group one of the plurality of code groups forwhich the association value exceeds a threshold; retrieving, from adatabase, a protocol range for the rate of utilization of the markercode group, the protocol range comprising upper and lower bounds for therate of utilization of the marker code group; generating, by the atleast one processor, at least one output identifying one or more of thefirst plurality of medical care providers having the rate of utilizationthat falls outside the protocol range.
 53. A method implemented on acomputer system of identifying medical care providers outside aprocess-of-care standard for a field of health care, the computer systemincluding at least one processor, the method comprising: retrieving, bythe at least one processor, medical care information for a firstplurality of medical care providers; calculating, by the at least oneprocessor, a code metric for one or more of the first plurality ofmedical care providers for each of a plurality of code groups, each codemetric for the code groups derived from the medical care information,wherein: each of the code groups in the plurality of code groupscomprises one or more related codes, and the code metric for each of theplurality of code groups is based on a rate of utilization of therespective code group by the medical care provider compared to the rateof utilization of the respective code group across the first pluralityof medical care providers, calculating, by the at least one processorfor each of the code groups, an association value based on a pluralityof pairs of values, each pair of values consisting of: an efficiencymetric associated with a particular medical care provider, and the codemetric associated with the respective code group for the particularmedical care provider, selecting, by the at least one processor as amarker code group, one of the plurality of code groups for which theassociation value exceeds a threshold; retrieving, by the at least oneprocessor from a database, second medical care information includingsecond claim line item information for treatment of at least 200,000patients over at least one year by a second plurality of medical careproviders; calculating, by the at least one processor, rates ofutilization of the marker code group for the second plurality of medicalcare providers across the at least 200,000 patients over the at leastone-year time period; determining, by the at least one processor, aprotocol range based on statistics derived from the rates of utilizationfor the second plurality of medical care providers, the protocol rangecomprising upper and lower bounds for the rate of utilization of themarker code group; and generating, by the at least one processor, atleast one output identifying one or more of the first plurality ofmedical care providers having the rate of utilization that falls outsidethe protocol range.
 54. The method in claim 53 which further comprises:generating, by the at least one processor for each of a plurality ofusers, a display including the marker code group and the statisticsderived from the rates of utilization for the second plurality ofmedical care providers; receiving, by the at least one processor fromeach of the plurality of users in response to the generated display,response data identifying a respective target range of usage in thedesignated medical care field of the one or more related codesassociated with the marker code group; and generating, by the at leastone processor using the response data from the plurality of users, theupper and lower bounds of the protocol range.
 55. The method in claim 54which further comprises: calculating, by the at least one processor, anaverage value of an upper end of the target ranges in the response dataand a minimum value of a lower end of the target ranges in the responsedata; setting the upper bound of the protocol range equal to the averagevalue; and setting the lower bound of the protocol range equal to theminimum value.
 56. The method in claim 55 wherein the average value is amedian.